5 Commits

Author SHA1 Message Date
dd3a77bc96 Apply UFMT to all files in benchmarks/ (#105928)
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/105928
Approved by: https://github.com/albanD
2023-07-26 01:18:48 +00:00
5ef023b05a [BE] Enable ruff's UP rules and autoformat benchmarks/ (#105429)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105429
Approved by: https://github.com/malfet
2023-07-19 04:46:37 +00:00
e08e93f946 Reland of benchmark code (#43428)
Summary:
Reland of the benchmark code that broke the slow tests because the GPU were running out of memory

Pull Request resolved: https://github.com/pytorch/pytorch/pull/43428

Reviewed By: ngimel

Differential Revision: D23296136

Pulled By: albanD

fbshipit-source-id: 0002ae23dc82f401604e33d0905d6b9eedebc851
2020-08-24 13:27:26 -07:00
74781ab5b8 Revert D23242101: [pytorch][PR] Implement first draft of autograd benchmark.
Test Plan: revert-hammer

Differential Revision:
D23242101 (c2511bdfa4)

Original commit changeset: a2b92d5a4341

fbshipit-source-id: bda562d15565f074b448022d180ec8f959c6ecc9
2020-08-21 12:22:57 -07:00
c2511bdfa4 Implement first draft of autograd benchmark. (#40586)
Summary:
It is quite a lot of code because I pulled some code from torchaudio and torchvision to remove issues I had to get latest version with pytorch built from source while I can't build there libs from source (dependency missing for torchaudio).

The compare script generates table as follows:
| model | task | speedup | mean (before) | var (before) | mean (after) | var (after) |
| -- | -- | -- | -- | -- | -- | -- |
| resnet18 | vjp | 1.021151844124464 | 1.5627719163894653 | 0.005164200905710459 | 1.5304011106491089 | 0.003979875706136227 |
| resnet18 | vhp | 0.9919114430761606 | 6.8089728355407715 | 0.019538333639502525 | 6.86449670791626 | 0.014775685034692287 |
| resnet18 | jvp | 0.9715963084255123 | 5.720699310302734 | 0.08197150379419327 | 5.887938499450684 | 0.018408503383398056 |
| ppl_simple_reg | vjp | 0.9529183269165618 | 0.000362396240234375 | 7.526952949810095e-10 | 0.00038030146970413625 | 7.726220357939795e-11 |
| ppl_simple_reg | vhp | 0.9317708619586977 | 0.00048058031825348735 | 5.035701855504726e-10 | 0.0005157709238119423 | 3.250243477137538e-11 |
| ppl_simple_reg | jvp | 0.8609755877018406 | 0.00045447348384186625 | 9.646707044286273e-11 | 0.0005278587341308594 | 1.4493808930815533e-10 |
| ppl_simple_reg | hvp | 0.9764100147808232 | 0.0005881547695025802 | 7.618464747949361e-10 | 0.0006023645401000977 | 6.370915461850757e-10 |
| ppl_simple_reg | jacobian | 1.0019173715134297 | 0.0003612995205912739 | 2.2979899233499523e-11 | 0.0003606081008911133 | 1.2609764794835332e-11 |
| ppl_simple_reg | hessian | 1.0358429970264393 | 0.00206911563873291 | 2.590938796842579e-09 | 0.0019975185859948397 | 2.8916853356264482e-09 |
| ppl_robust_reg | vjp | 1.0669910916521521 | 0.0017304659122601151 | 3.1047047155396967e-09 | 0.0016218185191974044 | 4.926861585374809e-09 |
| ppl_robust_reg | vhp | 1.0181130455462972 | 0.0029563189018517733 | 2.6359153082466946e-08 | 0.0029037236236035824 | 1.020585038702393e-08 |
| ppl_robust_reg | jvp | 0.9818360373406179 | 0.0026934861671179533 | 6.981357714153091e-09 | 0.00274331565015018 | 3.589908459389335e-08 |
| ppl_robust_reg | hvp | 1.0270848910527002 | 0.005576515104621649 | 3.2798087801211295e-08 | 0.005429458804428577 | 6.438724398094564e-08 |
| ppl_robust_reg | jacobian | 1.0543611284155785 | 0.00167675013653934 | 2.3236829349571053e-08 | 0.001590299652889371 | 1.2011492245278532e-08 |
| ppl_robust_reg | hessian | 1.0535378727082656 | 0.01643357239663601 | 1.8450685956850066e-06 | 0.015598463825881481 | 2.1876705602608126e-07 |
| wav2letter | vjp | 1.0060408105086573 | 0.3516994118690491 | 1.4463969819189515e-05 | 0.349587619304657 | 9.897866402752697e-05 |
| wav2letter | vhp | 0.9873655295086051 | 1.1196287870407104 | 0.00474404776468873 | 1.133955717086792 | 0.009759620763361454 |
| wav2letter | jvp | 0.9741820317882822 | 0.7888165712356567 | 0.0017476462526246905 | 0.8097219467163086 | 0.0018235758179798722 |
| transfo | vjp | 0.9883954031921641 | 2.8865864276885986 | 0.008410997688770294 | 2.9204773902893066 | 0.006901870481669903 |
| transfo | vhp | 1.0111290842971339 | 8.374398231506348 | 0.014904373325407505 | 8.282224655151367 | 0.04449500888586044 |
| transfo | jvp | 1.0080534543381963 | 6.293097972869873 | 0.03796082362532616 | 6.24282169342041 | 0.010179692879319191 |

Pull Request resolved: https://github.com/pytorch/pytorch/pull/40586

Reviewed By: pbelevich

Differential Revision: D23242101

Pulled By: albanD

fbshipit-source-id: a2b92d5a4341fe1472711a685ca425ec257d6384
2020-08-21 07:36:26 -07:00